Cardiac arrhythmias are a leading cause of morbidity and mortality in the United States. Abnormalities in heart rate, cardiac conduction (PR and QRS) and repolarization (QT) measured on the ECG predispose to the clinically important cardiac arrhythmias of atrial fibrillation (AF) and ventricular fibrillation (VF) / sudden cardiac death (SCD). We examine the genomic basis of these ECG endophenotypes in order to deconstruct arrhythmias into more proximate traits and discrete components, allowing us to better understand underlying mechanisms, provide insight into arrhythmia generation, and help target development of novel therapies. The molecular architecture of cardiac electrical activity and arrhythmias is not fully understood, but likely involves genomic, epigenomic, and environmental influences. Over the past 10 years, we have identified numerous common loci associated with cardiac electrical activity and arrhythmias, yet these common variants account for only a portion of the heritability of electrophysiologic and arrhythmic phenotypes. The agnostic examination of genotype-phenotype associations employed in genome- wide association studies (GWAS) does not incorporate knowledge of functional genomic regions or important biologic relationships. Additionally, we currently lack an understanding of the molecular mechanisms connecting mostly intergenic and intronic GWAS signals to phenotype. We therefore hypothesize that a systems biology approach integrating genetic sequence variation with omic data (epigenomic, transcriptomic, and proteomic data) will uncover novel associations and elucidate biologic mechanisms associated with arrhythmia-related phenotypes. We further hypothesize that examining the simultaneous association between sequence variation and multiple cardiac electrophysiologic phenotypes will help uncover additional novel mechanisms associated with cardiac electrical activity and arrhythmias. TOPMed's combination of rich phenotype data, with whole genome sequence (WGS), epigenomic, transcriptomic, and proteomic data, provides a unique opportunity to more comprehensively explore these hypotheses. We leverage sequence, omic, and phenotype data from multiple cohort studies to efficiently and cost-effectively examine and dissect association of omic factors with cardiac electrophysiology and arrhythmia risk. Our application is an ambitious yet eminently feasible effort that integrates clinical, genetic, and systems biology expertise. We aim to discover associations using omics data (Aims 1 and 2) and elucidate specific genes and biologic pathways underlying these associations (Aims 3 and 4). Our ultimate goal is to identify pathways, genes, and genetic variation that are clinically relevant, and therefore potentially the target of new therapies, diagnostics, or risk predictions.